With increasing demand for video streaming applications, exploiting cached content at the network edge becomes paramount to prevent congestion in the link between the wireless access network and the content providers. However, it is often challenging to exploit the caches in current client-driven video streaming solutions due to two key reasons. First, even those clients interested in the same content might request different quality levels as a video content is encoded into multiple qualities to match a wide range of network conditions and device capabilities. Second, the clients, who select the quality of the next chunk to request, are unaware of the cached content at the network edge. Hence, it becomes imperative to develop network-side solutions to exploit caching, in particular for the scenarios in which multiple video clients compete for some bottleneck capacity. In this paper, we propose EdgeDASH which is a network-side control logic running at a WiFi AP to facilitate the use of cached video content. In particular, an AP can assign a client station to a video quality different than its request, in case the alternative quality provides a better utility. This includes, for example, a function of bits delivered from the cache, video bit rate, and the buffer stalls. We formulate the quality assignment problem as an optimization problem and develop several heuristics with polynomial complexity. Our simulations show that EdgeDASH facilitates significant cache hits and decreases the buffer stalls only by changing the client’s request by one quality level, however with some increase in session instability. From our analysis, we conclude that EdgeDASH benefits are more visible especially when the clients with identical interests compete for a bottleneck link’s capacity, over the baseline where the clients determine the quality adaptation.
|Number of pages||14|
|Journal||IEEE transactions on network and service management|
|Early online date||12 Nov 2020|
|Publication status||E-pub ahead of print/First online - 12 Nov 2020|